v) Training Objective: Existing dialogue models learn the conditional distribution of the response given the context (either single- or multi-turn), from the maximum likelihood estimation (MLE) [1]}, [2]}, [3]}, [4]}. Due to the redundant nature of dialogue data and the greedy nature of MLE, the model usually learns just a simple mapping between the context and response, which yields generic responses. Alternative training frameworks that complement MLE with other constraints, such as generative adversarial networks, reinforcement learning, and variational auto-encoders, focus on modifying the conditional response distribution to encourage diversity [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}.
